Bank Effects and the Determinants of Loan Yield Spreads *

Li Hao**

September, 2003

JEL classification: G21.

Keywords: Loan yield spreads, bank characteristics, borrower characteristics, bank risk.

* The author is indebted to her supervisor, Professor Gordon S. Roberts, for his

continuous guidance and support. The author would like to thank her committee members, Professor Yisong Tian and Professor Melanie Cao, for their valuable suggestions and comments. All errors are the responsibility of the author.

** Ph.D. Candidate, Schulich School of Business, Finance Area, York University, 4700 Keele Street, Toronto, Ontario, Canada, M3J 1P3 Phone: 416-736-2100 Ext: 20635. Email: lhao@schulich.yorku.ca.

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Abstract

This paper examines the effects of bank characteristics on loan yield spreads after

controlling for borrower characteristics and non-yield-spread loan features. We assemble

loan contract variables, borrower and bank financial variables from the DealScan database,

the Compustat database and U.S. Federal Reserve Call Reports and incorporate a broader

range of bank characteristics to investigate bank effects on loan yield spreads. Bank

characteristics included in this study are bank size, monitoring power, and bank risk. In

addition, a new variable, the number of lenders in each loan contract, is introduced and

shown to be an important determinant of loan yield spreads. The measure of the number of

lenders in this study is different from those in prior studies as it focuses on the number of

lead lenders specified in each loan contract. Moreover, we define as lenders only those

banks that have lending relationships with borrowers and retain administrative, monitoring,

or contract enforcement responsibilities. We find evidence that bank characteristics

significantly influence loan yield spreads.

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1. Introduction

The lender-borrower relationship has long been studied in prior studies. There are two

sides of credit borrowing in the lender-borrower relationship, the demand side and the

supply side. One would expect that factors from both sides have effects on the

lender-borrower relationship. An important strand of research focuses on borrower effects

on this relationship and on the setting of loan contract terms. Taking the determination of

collateral as an example, Chan and Kanatas (1985) show that, in cases where the lender and

the borrower have different opinions about the borrower’s project, collateral will be offered

by the borrower when the lender’s valuation of the project is lower than the borrower’s.

Higher quality borrowers signal their creditworthiness by offering more collateral.

Besanko and Thakor (1987) also find a positive relationship between collateral and

borrower creditworthiness. In contrast, Berger and Udell (1990) and Harhoff and Korting

(1998) find a positive relationship between collateral and borrower risk in the context of

small business loans. Aside from collateral, a number of studies also examine the impact

of borrower characteristics on the determination of loan price (Angbazo, Mei and Saunders

(1998), Gorton and Kahn (2000), among others). In this strand of the literature, borrower

effects on the determination of loan contract terms have been widely explored while lender

effects have not.

Another strand of research addresses the effects of lender characteristics on loan

contract terms. Studying different types of financial intermediaries, Carey, Post and

Sharpe (1998) find evidence that compared to banks, finance companies seem to be more

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likely to make secured loans frequently and lend to riskier borrowers. Hubbard, Kuttner

and Palia (2002) incorporate another lender attribute, bank financial health, and show that

low-capital banks tend to charge higher loan rates than well-capitalized banks. Coleman,

Esho and Sharpe (2002) examine further lender characteristics, and state that bank

monitoring ability, bargaining power, risk and syndicate structure have significant

influence in determining loan maturity and pricing. However, studies about lender effects

on the lender-borrower relationship and especially the determination of loan contract terms

remain scarce in this literature.

This paper is an empirical study of bank effects on the setting of loan prices, taking

into account the influences of both bank and borrower attributes. Further lender

characteristics are included in this study of bank effects on loan prices. Notably, a new

dimension of bank characteristics, the number of lenders at the loan level, is introduced.

This is motivated by recognizing the important influence of single versus multiple banking

relationships documented in prior studies. It has been well documented that the number of

banking relationships a borrower maintains at a given moment plays an important role in

the lender-borrower relationship. Petersen and Rajan (1994) find that small firms

borrowing from multiple banks are of lower creditworthiness than those borrowing from a

single bank. For such firms, borrowing from fewer banks generally increases credit

availability and lowers the cost of funds. Houston and James (2001) observe that firms

relying on a single bank exhibit greater sensitivity of investment to cash flow than firms

maintaining multiple bank relationships or borrowing from public debt markets. Harhoff

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and Korting (1998) empirically investigate the role of lending relationships in determining

the costs of external funding, and document that the number of relationships increases with

the firm’s age, size, and leverage. Carletti (2000) studies the link between the number of

bank relationships and banks’ incentives to monitor, along with the effect of such link on

loan rates and firms’ choice between single and multiple relationships.

Recognizing the important influence of the number of lenders on the lender-borrower

relationship, the current paper incorporates a new variable, the number of lenders at the

loan level, along with other lender characteristics, to examine bank effects on loan yield

spreads. The number of lenders at the loan level is expected to affect the setting of loan

contract terms. The effects of risk diversification, monitoring duplication, negotiation

complexity and bargaining power constitute the main issues. As for multiple-lead-lender

loans, the negotiation process between a borrower and multiple lenders becomes more

complex than is the case with a single lender. Considering the potential for monitoring

duplication and benefit sharing in cases of loans with multiple lenders, it is expected that

the lenders’ monitoring effectiveness is affected by the presence of multiple lenders, as is

the settings of loan contract terms. The bargaining power of each party will change

according to its level of commitment to the loan contract. Furthermore, the presence of

multiple lenders in a given loan contract could suggest that there is unfavorable

information about the borrower and thus that the original bank is unwilling to lend to the

borrower on its own. Including more lenders in a loan contract could diversify risk and

reduce each lender’s exposure to firm-specific risk, while also serving to discourage

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strategic default on the part of the borrower (Esty and Megginson (2003)). In brief, the

number of lenders at the loan level is expected to affect the setting of loan contract rates.

This study extends the existing literature by emphasizing the significant influence of

bank characteristics on loan yield spreads and provides evidence that borrower

characteristics are important determinants of loan yield spreads. Bank effects on loan yield

spreads are examined after controlling for the effects of borrower and non-yield-spread

loan characteristics. Our main findings are that banks with greater monitoring power and

riskier banks with lower capital-asset ratios extract higher rents, which is consistent with

the findings in prior studies. Importantly, the new dimension of lender characteristics – the

number of lenders for a given loan contract – is shown to have a significant influence on

loan yield spreads. The positive relationship between the number of lenders at the loan

level and loan yield spreads suggests that the presence of multiple lenders is associated

with the duplication of monitoring, complex negotiation processes, the possibility of

unfavorable information about the borrower, and the intention to discourage borrowers’

strategic default.

The contributions of this paper to the existing literature are threefold. First,

considering influences from both the demand side and the supply side of credit borrowing,

we assemble bank and borrower financial variables in order to fully investigate bank

effects on the determination of loan yield spreads, controlling for the effects of

non-yield-spread loan features and borrower characteristics. Second, we incorporate a new

dimension of bank characteristics in the study of bank effects on loan yield spreads.

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Specifically, we introduce the number of lenders at the loan level as a variable affecting the

setting of loan yield spreads, along with bank size, bank risk, and bank monitoring power.

Third, continuing our focus on the role of multiple lenders, in the case of syndicated loans

we include all lead banks for a given loan contract in this study. For multiple-lead-bank

syndicated loans, we assign each lead bank a weight according to its contribution to the

loan facility based on its share of the syndicated loan, data which is available from

DealScan. By so doing, we avoid omitting data on multiple lead banks which provide

valuable information about bank effects on loan yield spreads. In contrast, Coleman, Esho

and Sharpe (2002) study only the lead bank which contributes the largest portion of the

syndicated loan. This could lead to a biased understanding of the effects of lender

characteristics due to the omission of substantial lender information in the case of

multiple-lead-bank syndicated loans.

The remainder of the paper is organized as follows. In the next section, we discuss

proxies for bank, borrower, and non-yield-spread loan characteristics. The central testing

hypotheses in this study are also discussed in section II. Section III describes the data and

the empirical approach we use. Our empirical tests are reported in Section IV. Section V

concludes.

2. Bank, borrower, non-price loan characteristics, and testing hypotheses

The objective of this paper is to examine bank effects on the determinants of loan yield

spreads, while controlling for the effects of borrower characteristics and non-yield-spread

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loan features on loan yield spreads. In the following, we discuss our hypotheses along with

proxies we use for bank, borrower, and non-price loan characteristics.

2.1 Proxies for bank characteristics

Number of lenders

Prior studies have documented the influence that the number of lenders maintained by

a borrower exerts on loan contract terms. Most of the studies define the number of lenders

as the number of banking relationships the borrower keeps up through borrowing and cash

management activities. By contrast, this study defines the number of lenders as the number

of lead banks in a given loan contract by employing detailed loan-level data. Put another

way, we are concerned with the number of lenders at the level of the individual loan

contract, whereas prior studies have defined the number of lenders as the number of banks

with which a borrower has borrowing relationships.

In prior studies, Petersen and Rajan (1994) use the number of banks from which the

firm borrows as a measure of borrower concentration. One of their main findings is that

borrowing from multiple lenders leads to increases in credit prices and decreases in the

availability of credit. The number of banks with which a borrower maintains relationships

can therefore serve as a proxy for the borrower’s quality, but not in a strict way. Syndicated

loans, however, are not included in their study because they focus on small borrowers.

Ongena and Smith (2000) define the number of bank relationships in terms of cash

management services. In their data set, some of the recorded bank relationships are not

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traditional lending relationships. Cash management services include the collection of

deposits, the management of bank balances and overdrafts, foreign exchange management,

and many other services. Detragiache, Garella, and Guiso (2000), using Italian data for

empirical testing, argue that a borrower has a bank relationship if it borrows from a bank

under one of the following six loan categories: commercial paper discounted by the bank,

lines of credits, export loans, collateralized loans, medium-term loans, and long-term loans.

One of the most important sources of debt financing, the syndicated loan, is not included in

this list. In this study, we only define as lenders those banks that have lending relationships

with borrowers and retain administrative, monitoring, or contract enforcement

responsibilities.

As discussed above, the measure of the number of lenders differs from those used in

prior studies as it focuses on the number of lenders specified in each loan contract. The

number of lenders in this study is specified in each term loan facility (contract), and is

different from the current number of banking relationships the borrower maintains. Finally,

for the purposes of this study, the relationship between the bank lender and the borrower is

limited to traditional lending business and does not include cash management services.

One advantage of this study is the focus on syndicated loans, one of the most important

sources of funding for medium and large borrowers. We employ the DealScan database

which provides detailed information on loan contract features.1 In a syndicated loan, a

1 Other studies using DealScan for various research purposes include Carey, Post, and Sharpe (1998), Dennis and Mullineaux (1998), Dennis, Nandy and Sharpe (2000), Hubbard, Kuttner, and Palia (2002).

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group of financial intermediaries agree to jointly issue a loan to a borrower. Dennis and

Mullineaux (2000) document that in syndicated loans, “One lender will typically act as

managing agent for the group, negotiating the loan agreement, then coordinating the

documentation process, the loan closing, the funding of loan advances, and the

administration of repayments.” Lenders acting as managing agents retain administrative,

monitoring and contract enforcement responsibilities. These agents are assumed to have

relationships with borrowers, while the participating members are less likely to have such

relationships with the borrower since they are not generally involved in the negotiations

with or active monitoring of the borrower. In the case of syndicated loans with a single

lead bank responsible for negotiating the loan contract with the borrower, we view these as

loans with a single lender. Furthermore, there are some situations in which several banks

assume the roles of originator, loan administrator, and collateral administrator separately.

Under these circumstances, the syndicated loan is treated as a loan with multiple lenders.

In our sample, 66.39% of the syndicated loans have two or more lead banks. We treat

the number of lead banks in multiple-lead-bank syndicated loans as the number of lenders.

It is expected that the presence of multiple lead banks affects the lenders’ monitoring

effectiveness and thus the setting of loan contract rates due to the potential for monitoring

duplication and benefits sharing. Also, unfavorable information about the borrower could

be inferred from the presence of multiple lead banks, and thus the original bank is

unwilling to lend to the borrower on its own. Given these concerns (duplication of

monitoring, sharing of benefits, potential unfavorable information about the borrower, and

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complexity of the negotiation process), including more lenders in a given loan contract

could result in higher loan rates. On the other hand, including more lenders in a loan

contract could diversify risk and reduce each lender’s exposure to firm-specific risk, while

also serving to discourage borrowers’ strategic default (see Esty and Megginson(2002)).

Following this line of reasoning, one would expect that multiple lenders might result in

lower loan rates. However, considering that the typical borrowers in this study’s sample

are more likely to be medium or large-sized firms, which are supposed to be of higher

quality and less prone to financial distress, we would expect the number of lenders to have

a positive relation with loan yield spreads.

Bank size

The benefits of bank size have been widely documented in the literature of bank

mergers and acquisitions (Kane (2000) and Milbourn, Boot, and Thakor (1999), among

others). Mergers and acquisitions activity in banking has been intense in the last decade in

many countries. A clear outcome of the bank merger trend is a tremendous increase in

bank size. Larger banks have greater market power and better access to government safety

net subsidies relative to smaller banks. Relatively smaller banks may be at a competitive

disadvantage in attracting the business of larger loan customers. Not surprisingly, bank

size influences a bank’s lending activities.

In its explorations of the effects of bank size on loan yield spreads, this study employs

a measure of relative size which is defined as the ratio of bank size to borrower size, similar

to that used in Coleman, Esho and Sharpe (2002). Bank size and borrower size are the

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natural logarithm of bank and borrower’s total assets. The calculation of bank size in this

study differs from that of Coleman, Esho and Sharpe (2002). In this study, to incorporate

all lead banks’ features in the calculation of bank size, we assign each lead bank a weight

based on its portion of the shares held by lead banks in each loan facility. Each lead bank’s

total assets is then multiplied by its weight, and the sum of all the lead banks’ weighted

assets is used to calculate bank size for each loan facility. In contrast, in Coleman, Esho

and Sharpe (2002), the total assets of the bank which contributes the largest portion of the

loan is used for calculating bank size. This approach, by design, omits other lead banks’

features from the calculation of bank size. Coleman, Esho and Sharpe (2002) use relative

size as a proxy for bank bargaining power vis-à-vis the borrower. The negotiation process

is a bilateral interaction involving the bank and the borrower. Bargaining power is to a

large extent dependent on asymmetric information between the bank and the borrower, and

also on the competence of outside banks. As delegated monitor, bank lenders have

incentive and capability to collect information at a lower cost. Given their monopoly of

borrower information, there is the potential that bank lenders may “hold up” the borrower

by threatening to liquidate the borrower’s project. The size of the bank relative to the

borrower has explanatory power for bargaining power, but this power is probably limited.

In this study, we use relative size ratio in empirical testing to examine the effects of bank

size on the determinants of loan yield spreads. Following the argument in Coleman, Esho

and Sharpe (2002), it is expected that the relative size ratio is positively related to loan

yield spreads.

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Bank monitoring power

The role of banks in information production and monitoring of borrowers in the credit

allocation process has been widely explored. Since they are responsible for monitoring

and screening loan contracts, banks have the ability to mitigate adverse selection and moral

hazard problems, and provide flexibility by reconstructing loan contracts. Billet, Flannery

and Garfinkel (1995) use bank credit rating as a proxy for bank monitoring effectiveness.

They argue that high quality banks attempt to maintain their credit rating because a higher

credit rating is associated with higher bank profits, which are the result of their

effectiveness in monitoring corporate borrowers. Therefore, a bank’s credit rating could be

used as a proxy for its monitoring power. Coleman, Esho and Sharpe (2002) use the salary

fixed effect, defined as the ratio of salary and benefits to total operating expense, as a proxy

for monitoring ability. They assume that staff abilities in monitoring activities is reflected

in their salaries, so that the salary expense ratio will mirror the resources invested in

monitoring activity and the competence of bank staff. In an alternative approach, Johnson

(1997) uses loan loss provisions to proxy reputation in bank monitoring abilities, arguing

that a change in loan loss provisions indicates a change in management’s assessment of

loan portfolio quality and/or a change in monitoring and screening abilities.

It is difficult to directly measure bank monitoring power because monitoring and

screening activities are largely unobservable. Since banks attempt to maintain their

reputations through appropriate loan issuing and proper monitoring activities, we may

infer bank monitoring power from banks’ reported measures of loan quality, such as loan

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loss provisions. Loan loss provisions are recorded as losses of loan principal that are

probably and reasonably estimable. It is the responsibility of the bank’s management to

determine an adequate loan and lease loss provision based on current knowledge of the

bank's loan portfolios, and to maintain a reviewable record of the basis for their

determination of loan and lease loss provisions. Bank management has superior

information about default risks in its loan portfolios compared to investors and other

stakeholders. Therefore, we assume that an assessment of bank management may be a

more accurate indication of bank monitoring power, and that decisions on the level of loan

loss provisions may convey information about the quality of bank monitoring activities.

As such, we employ the loan loss provision as the proxy for bank monitoring power in this

study.

Monitoring and information production, two main advantages of bank debt relative to

public debt, provide banks with an information monopoly which might be used to extract

higher rents. According to “hold-up” theory in Rajan (1992), one would expect that a bank

with superior monitoring power might extract higher rents. In other words, bank lenders’

monitoring power is positively related to loan yield spreads. As the proxy for bank lenders’

monitoring power, loan loss provisions are regarded as negatively associated with the

intensity of bank monitoring activities. The loan loss provision is thus expected to be

inversely related to loan yield spreads; the higher the loan loss provisions, the lower the

loan yield spreads.

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Bank risk

Bank lenders are exposed to many risks in doing business. These risks are operational

and financial, domestic and international, as well as on- and off-balance-sheet. In reality,

these risks are often interdependent. Liquidity risk, arising from the uncertainty of the

timing of bank cash flows, is one of the risks that bank lenders face in their business.

Banks with seriously impaired capital will find it extremely difficult to raise funds to

replace maturing liabilities. Liquidity risk is a crucial concern for bank lenders. In this

study, we focus on the effects of bank liquidity risk on the determinants of loan yield

spreads.

As a proxy for bank risk, Hubbard, Kuttner and Palia (2002) choose the capital-assets

ratio, arguing that a riskier bank will have a lower capital-assets ratio and charge a higher

premium. They find that the cost of borrowing from low-capital banks is higher than the

cost of borrowing from well-capitalized banks, even after controlling for borrower risk and

information costs. Following the argument in Hubbard, Kuttner and Palia (2002), in this

study we also use the capital-assets ratio (equity capital/total assets) to measure bank

liquidity risk. We presume that banks with higher capital-assets ratios have less liquidity

risk, and banks with less liquidity risk charge lower premia. This suggests that a bank’s

capital-assets ratio is negatively related to loan yield spreads.

2.2 Proxies for borrower and loan characteristics

The effects of borrower characteristics on borrowers’ investment decisions have been

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well explored. Those borrower characteristics which are more closely related to debt

agency problems are the main characteristics investigated in prior studies. There are two

primary debt agency problems identified; one is the risk-shifting or asset substitution

problem (Jensen and Meckling (1976)), the other is the under-investment problem (Myers

(1977)) (Mao (2003)). Mayers (1977) states that, with increases in the firm’s leverage,

equity holders have incentives to under-invest in positive NPV projects. Being aware of

these debt agency problems, debt holders price the debt appropriately and can be expected

to demand higher returns. Moreover, Chemmanur and Fulghieri (1994) find that firms

with a greater probability of encountering financial distress tend to choose bank loans

over public debt because banks may be amenable to renegotiating contract terms in the

event of financial distress. Houston and James (1996) document that firm size, the

importance of growth opportunities, overall leverage, the number of bank relationships,

and a firm’s access to public debt markets influence a firm’s decision to borrow from

banks. Particularly, they show that reliance on bank borrowing decreases with firm size

and reductions in overall leverage.

Given the important influences of borrower characteristics such as leverage, firm size

and firm solvency, on firms’ investment decisions, we include borrower leverage

(debt/assets), borrower size (natural logarithm of total assets), and borrower current ratio

(current assets/current liabilities) to examine borrower effects on loan yield spreads.2

2 The primary proxy for borrower leverage is the ratio of total debt (long-term debt plus debt in current liabilities) to borrower total assets (book value) (See Hubbard, Kuttner and Palia (2002) and Shane (2003)).

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These borrower variables serve as proxies for two groups of borrower characteristics:

borrower risk and information costs.3 One would expect that borrower size is negatively

related to loan yield spreads since smaller firms are assumed to have higher risk due to

higher information costs, and larger firms are likely to be more diversified, which implies

lower expected bankruptcy costs and lower risk. This is consistent with the findings of

Peterson and Rajan (1994), who posit that adverse selection and moral hazard may have

more influence on small and young corporate borrowers. We include book-value

measures of leverage (debt/assets) as the proxy for borrower risk. As borrowers with

higher leverage ratios likely have higher risk, borrower leverage is expected to be

positively related to loan yield spreads.

To control for the effects of non-price loan characteristics on the determinants of loan

yield spreads, we include the facility amount size, term facility maturity, a dummy

variable indicating the loan’s secured status, and a dummy variable indicating whether the

loan is syndicated. If the loan is underwritten by a syndicate, it is more likely to be

successfully distributed and associated with lower risk. This is equivalent to a reduction

in syndication risk for the originating bank(s) and a reduction in the firm-specific risk

associated with individual loans. This suggests that lower yield spreads are expected on

syndicated loans. Therefore, the syndicated loan indicator dummy variable is expected to

be negatively related to loan yield spreads. Loans with longer maturities are assumed to

3 One could argue that these three borrower variables cannot completely capture all of the borrowers’ characteristics. Considering that debt agency problems are more severe for small and risky firms (Myers (1977)), we control for the effects of borrower characteristics associated with borrower risk and information costs to investigate bank effects on loan pricing (Coleman, Esho and Sharpe (2002), Hubbard, Kuttner and Palia (2002)).

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be associated with firms with higher credit quality. Borrowers with lower credit quality

are limited to shorter-maturity loans and pay higher loan yield spreads due to higher

potential default risk. This suggests that maturity is negatively related to loan yield

spreads. Borrowers who have to pledge collateral are associated with higher firm-specific

risk; collateral may therefore be regarded as a signal of high risk. (Berger and Udell

(1990), Harhoff and Korting (1998)). As such, the secured status is likely to be positively

related to loan yield spreads.4 Loan size is viewed as an important determinant of loan

yield spreads. Larger loans are more likely to be associated with large borrowers, for

whom more information is available. The presence of more information about these firms

tends to reduce lenders’ costs of monitoring, and for this reason large borrowers might be

charged lower loan yield spreads. As a result, a negative relationship between the size of

the loan and loan yield spread is expected. A summary of the model and the expected

signs of the estimated coefficients are provided in Table 1. The endogenous

characteristics of these explanatory variables might be a concern. We will address this

issue later in the robustness checks section.

[Table 1 here]

3. Data Our interest is in the effects of bank characteristics on the determinants of loan yield

spreads. Therefore, we need to isolate the effects of bank characteristics, borrower

4 However, in Chan and Kanatas (1985) and Besanko and Thakor (1987), higher quality borrowers pledge collateral, thereby signaling their creditworthness. Moreover, one could argue that loans secured by a pledge of specific assets or equity are associated with lower risk of principal and interest default, resulting in lower yield spreads.

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characteristics and non-price loan characteristics on loan yield spreads. Information on

banks, borrowers and loans for each loan contract is required. Our main data sources are

the DealScan database, the Compustat database and U.S. Federal Reserve Call Reports.

Supplied by the Loan Pricing Corporation (LPC), the DealScan database includes

borrower identity and location; lender identity, lenders’ shares and lender roles5; loan

purpose, type, amount and contract date; and price, as well as a number of non-price

terms.6 The DealScan database provides relatively little detail relating to the borrower’s

financial position and the lender’s financial position. Financial variables reflecting

borrower characteristics can be obtained from the Compustat database, while lender

characteristics are available in the Call Reports provided by the U.S. Federal Reserve. The

Call Reports are the regulatory filings that all commercial banks having insured deposits

submit each quarter. The Call Reports include detailed information on the composition of

bank balance sheets and some additional data on off-balance-sheet items. These data are

reported at the level of the individual bank.

In this study, the date of the facility is used as a key variable to match the annual report

data of banks and borrowers with the year-end data immediately preceding the facility date.

We obtain loan data for 1988-1999 from the DealScan database, bank data for 1987-1998

from the Call Reports, and borrower data for 1987-1998 from the Compustat database. As

for lender type, we include bank lenders only and exclude other types of lenders such as 5 From the DealScan database, the lender role is divided into the following types: Participant, Advisor Only, Co-agent, Co-arranger, Co-manager, Co-lead Manager, Co-Syndications Agent, Secondary Investor, Sub-participants, Technical Agent, Collateral Agent, Administration Agent, Agent, Arranger, Documentation Agent, Lead Bank, Lead Manager, Manager, Managing Agent, Sole Lender, Sr. Lender Manager, Sr. Managing Agent, Syndications Agent. 6 In DealScan, some of the “deals” involve more than one loan “facility” originated by the same borrower on that date. In this study, we conduct our analysis at the facility-level, treating each facility as a separate loan. This is because deals with multiple lenders do not always involve the same group of lenders in all facilities. Moreover, loan yield spreads are dependent on facility-level attributes.

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insurance companies, mutual funds, etc. We begin with an extraction of the DealScan

database which contains data on 65,380 loan facilities originated by U.S. banks from 1988

to 1999. To ensure the availability of borrower information, we require that the borrower’s

country of origin is the USA and delete observations with missing borrower names and/or

borrower tickers. Also, we exclude loan facilities without lenders’ names or loan facility

active date. We are left with 19,082 loan facilities after applying these filters. Using the

names of the borrowers and locations recorded in DealScan, we match the loan data with

firm data from the Compustat database. In total, 10,839 loan facilities are successfully

matched. Next, we use the names of lead banks in DealScan to link matched loan and

borrower information with bank-level information in U.S. Federal Reserves Call Reports.

The U.S. Federal Reserve’s Call Reports supply many financial, structural and

geographical variables for bank lenders. For syndicated loans, we assume that the lead

bank’s characteristics have the greatest effects on the determination of loan contract terms

because the responsibilities for bargaining, monitoring and screening are placed on the lead

banks.7 Considering the duties of the lead bank (origination, loan administration, collateral

administration, etc.) in syndicated loans, we include lead banks in our sample.8 In the case

of a loan with multiple lead banks, we assign each lead bank a weight according to its

7 Dennis and Mullineaux (2000) document that the agent bank negotiates and drafts all the loan documents; participants can provide comments and suggestions but are not generally involved in the negotiations with the borrower. In some transactions, agent roles (origination, loan administration, collateral administration) are divided among several institutions. Fees are split in the case of multiple agents. Angbazo, Mei and Saunders (1998) state that lead banks retain primary administrative, monitoring, and contract enforcement responsibilities. Banks acting as managers perform administrative oversight duties although their share ownerships in the syndicated loan are on average smaller than lead banks. Participants do not perform special functions other than being signatories to the original loans. 8 We exclude banks whose lender role in a syndicated loan is that of participant, advisor only, secondary investor, sub-participants, technical agent, or collateral agent in syndicated loans.

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portion of the total shares held by lead banks. In doing so, we include information about all

lead banks in each loan facility, avoiding the exclusion of valuable bank information. After

matching with bank data, 1,869 loan facilities with usable information remain. A large

number of observations are lost in the process of linking the loan information with

bank-level data because many bank names could not be found in Call Reports. Finally, we

drop 17 observations identified as outliers because the logarithm of borrower total assets is

less than zero.

The remaining sample data consist of 1,852 loan facilities associated with 95 banks

and 740 firms. Each firm, on average, has more than 2 loan facilities in this sample. Since

the DealScan database covers the loan syndication market, in our final sample 71.98% of

loan facilities are syndicated loans and 28.12% are sole-lender loans. For comparison’s

sake, in the full DealScan sample 79.83% of loan facilities are syndicated loans and

20.17% are sole-lender loans. Table 2 provides a description of all the explanatory and

dependent variables used in the cross-sectional analysis of the effects of bank, borrower,

and non-price loan characteristics on loan yield spreads.

[Table 2 here]

Before investigating empirically the effects of bank characteristics on the determinants

of loan yield spreads, we begin by documenting the summary statistics of selected

variables in Table 3.

[Table 3 here]

As shown in Table 3, the loan yield spread (RATEAISD), measured by rates

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all-in-spread drawn, is on average 178 basis points above the benchmark London interbank

offering rate (LIBOR).9 In DealScan, all-in-spread drawn is expressed as a spread over

LIBOR which takes into accounts both one-time and recurring fees associated with the

loan. The all-in-spread drawn is thus defined as the coupon spread, plus any annual fee,

plus any up-front fee divided by the maturity of the loan. For loans not based on LIBOR,

the LPC converts the coupon spread into LIBOR terms by adding or subtracting a constant

differential reflecting the historical averages of the relevant spreads. In this sample the

average maturity of the loan facilities (TFCMAT) is 3.62 years, and the mean loan facility

size is $0.27 billion

This study is similar in spirit to the research of Coleman, Esho and Sharpe (2002), in

which the influence of bank characteristics on loan pricing and maturity is examined. Even

after controlling for borrower and non-price loan characteristics, they find that bank

characteristics (bank monitoring ability, bargaining power, risk, and syndicate structure)

significantly affect the setting of loan maturity and pricing. Our study extends Coleman,

Esho and Sharpe (2002) in two important regards. First, the number of lenders in each loan

contract is not included in their study. We incorporate the number of lenders in each loan

contract as one of the bank characteristics and predict a significant effect on the

determinants of loan yield spreads. Second, we include multiple lead bank characteristics

in the investigation of bank effects on the determination of loan yield spreads. To do so, we

9 Other studies using the “all-in-spread drawn” to measure loan yield spreads include Angbazo, Mei and Saunders (1998), Hubbard, Kuttner and Palia (2002) among others. Loans are frequently priced off the prime rate, 6-month LIBOR, and 6-month Certificate of deposit (CD) rates.

22

assign each lead bank a weight according to its contribution to the loan contract based on

its share of the syndicated loan.

The summary statistics comparison of selected variables from our sample and the

sample in Coleman, Esho, and Sharpe (2002) is presented in Table 4. Table 4 shows that

the bank lenders in this sample have mean total assets of $ 32 billion, mean total loans (net

of unearned income) of $ 21.6 billion, and mean total deposits of $22.6 billion, with a mean

cash/assets ratio 8%. Meanwhile, the borrowers have mean total assets of $2.9 billion, and

mean sales of $ 1.9 billion.

[Table 4 here]

As shown in Table 4, the mean relative size ratio in this sample is 1.88 which is much

smaller than the mean relative size ratio of 437.10 in the Coleman, Esho, and Sharpe (2002)

sample. One of the reasons for such a big difference is that, in Coleman, Esho and Sharpe

(2002)’s sample, only the lead bank contributing largest portion to each loan facility is

included while we include all lead banks in each loan facility in our sample. The typical

banks in the present sample are much smaller, and the typical borrowers are much larger in

terms of total assets, than those in the sample of Coleman, Esho and Sharpe (2002).

Similarly, the facility amount size in this sample is greater than that in the Coleman, Esho

and Sharpe (2002) sample. Moreover, 71.98 % of loan facilities in this sample are

syndicated loans while 92% of the sample loans are syndicated loans in the Coleman, Esho,

and Sharpe (2002) sample. These sample differences might lead to the differing signs of

the coefficients of the relative size ratios in the empirical results of this study and Coleman,

23

Esho, and Sharpe (2002). Discussion of the sign of the coefficient of relative size ratio will

be presented later in the interpretation of empirical results.

Empirical methodology

In this study, we focus on the effects of bank characteristics, which include the number

of lenders, bank monitoring power, bank size, and bank risk, while controlling for the

effects of borrower characteristics and non-yield-spread loan features. Defining loan yield

spreads as a function of bank, borrower and non-yield-spread loan features, we examine

the effects of bank lenders, borrowers, and non-price loan characteristics on the

determinants of loan yield spreads, using ordinary least squares.

Table 5 contains a matrix of Pearson correlation coefficients among the dependent and

explanatory variables. These correlations reveal some simple relationships among the

variables.10 The relative size ratio is positively correlated with loan yield spreads. Both

loan loss provision and capital assets ratio are negatively correlated with loan yield spreads.

This is consistent with the idea that banks with intensive monitoring power and less bank

risk extract a higher spread for their monitoring and lending activities.

[Table 5 here]

10 To detect multicollinearity, we apply variance inflation factor (VIF) which can be expressed as VIF=1/ (1-R-square). A general rule is that the VIF should not exceed 10 (Belsley, Kuh, & Welsch, 1980). In this study, we use each explanatory variable as the dependent variable to run a regression and obtain the R-square and VIF. None of the VIFs we obtained exceed 10.

24

4. Empirical results

Table 6 presents the results of the examination of the relationships between loan yield

spreads and bank characteristics, borrower characteristics, and non-yield-spread loan

features. We compute White’s (1980) heteroskedasticity-consistent standard errors to

account for heteroskedaticity.

[Table 6 here]

In this study, we are interested in the effects of bank characteristics on the determinants

of loan yield spreads. As shown in Table 6, regression 1 reports the results without

considering the effects of bank characteristics, while the variables reflecting bank

characteristics are included in regression 2. The pure effects of non-price loan

characteristics on loan pricing are examined in regression 3. Bank effects and non-price

loan characteristics are incorporated in regression 4. It is clear that the regression

equations as a whole are significant based on the F-values. Comparing the adjusted R

squares in these four regressions, it is clear that the inclusion of bank characteristics among

the explanatory variables in regressions 2 and 4 improves the entire model’s explanatory

power. Moreover, most of the coefficients of variables reflecting bank characteristics are

statistically significant. This suggests that bank characteristics have significant effects on

the determinants of loan yield spreads. We next discuss in detail the effects of bank,

borrower and non-yield-spread loan characteristics on the determinants of loan yield

spreads, focusing on the regression in Table 6.

25

4.1 Bank Effects

The coefficient of the relative size ratio, which is the ratio of bank over borrower size,

is significantly negative. This result is inconsistent with the finding in Coleman, Esho and

Sharpe (2002). In their study, they find relative size is positively related to loan yield

spreads and negatively related to maturity, while in this study the coefficient of relative size

ratio is negative.

The difference in the results might arise from the different relative size ratios in these

two samples. In the current sample, the mean relative size ratio is 1.88, which is much

smaller than the 437.10 calculated for the Coleman, Esho and Sharpe (2002) sample. The

typical bank in our sample has mean total assets of $ 31.98 billion while the typical bank in

the Coleman, Esho and Sharpe (2002) sample has mean total assets of $ 90.60 billion.

Since only the lead bank contributing the largest portion of the loan for each loan facility is

included in Coleman, Esho and Sharpe (2002), there are only 52 lead banks represented in

their sample with the top 5 banks appearing in 76 % of the sample.11 In our sample, we

include all lead banks in each loan facility. In total, 95 banks are presented in our sample.

Our reliance on the Compustat database for borrower characteristics suggests that

small firms might be underrepresented in our sample. The typical borrower in this sample

has mean total assets of $ 2.91 billion compared to borrower mean total assets of $ 0.83

billion in Coleman, et al (2002). Borrowers in this sample are more likely to be medium or

large-sized firms in terms of total assets. Thus, it is expected that these borrowers are more 11 These top five banks are Bank of America, Chase Manhattan Corporation, Citibank, Bank One Corporation and Fleet Boston Corporation (Coleman, Esho and Sharpe (2002).

26

likely to have less firm-specific risk and thus enjoy lower loan yield spreads. Moreover,

the typical lenders corresponding to these borrowers are not large banks in terms of total

assets. Following the bargaining power argument in Coleman, Esho and Sharpe (2002),

the borrower’s bargaining power is expected to increase with firm size. Taken together, the

typical borrower in this sample is a medium or large-sized firm with less firm-specific risk.

Thus, it is not surprising to observe the negative sign of the coefficient of the relative size

ratio.12

As shown in Table 6, the coefficient of bank loan loss provisions is significantly

negative as expected. Loan loss provision is used as the proxy for bank monitoring power

and is inversely related to bank monitoring power. A decrease in the level of loan loss

provisions indicates an increase in managerial assessment of loan portfolio quality and

enhanced bank monitoring and screening abilities. For a bank, a lower level of loan loss

provisions conveys favorable information about the bank’s quality of monitoring activities.

Therefore, banks with low levels of loan loss provisions have superior monitoring power

and thus charge higher premia.

The significantly negative coefficient of the bank lenders’ capital-assets ratio indicates

that a bank with a lower capital-assets ratio charges higher loan yield spreads. This result is

12 The coefficient of the relative size ratio is not significant, as shown in regression 4 in Table 6. Our argument focusing on the issue of size may arguably be too narrow. An alternative interpretation for the negative relation between the relative size ratio and loan yield spreads can be inferred from the following. Empirical studies of the U.S. banking industry document the significant effects of a bank’s size on its lending business. Larger banks are more likely to lend to medium and large companies, assuming these borrowers having less firm-specific risk. Given that banks prefer lending to big borrowers, if a big bank lends to a small borrower then one would expect that the small borrower must have high credit quality and strong bargaining power in order to borrow from the bank. As such, we presume that those small borrowers who borrow from big banks are associated with a lower risk premium, and thus the relationship between the relative size ratio (lender size over borrower size) and loan yield spreads is negative.

27

supporting evidence for the contention that a more risky bank with a lower capital-assets

ratio will charge a higher premium.13 This result is consistent with our expectation and

also with the findings in Hubbard, Kuttner and Palia (2002) and Coleman, Esho and Sharpe

(2002).

The coefficient of the number of lenders is significantly positive and relatively large in

magnitude. The positive sign of the coefficient indicates that the presence of more lenders

in the loan facility reflects the originating bank’s unwillingness to lend to the borrower on

its own and could suggest the existence of unfavorable information about the borrower’s

credit quality. Dennis and Mullineaux (2000) argue that in the case of syndicated loans, the

agent bank may have information unavailable to the syndicated participants. The

originating bank is willing to syndicate those loans on which it has less favorable “inside

information”. Simons (1993) examines empirically the motives for syndications and

reports that diversification is the primary motive for syndication. Thus, one would expect

that the fact that syndicates are more diffuse reflects to some extent that there is less

favorable information about the borrower. Moreover, in syndicated loans, lead banks hold

large portions of the loan and therefore have an incentive to monitor, while lead banks

holding smaller stakes (such as managers) may be likely to engage in a free ride since

monitoring is costly. From the perspective of bank governance functions, Esty and

Megginson (2002) state that a loan syndicate with a large number of lenders can deter

13 As a robustness check, we have also rerun the regression with alternative proxies for bank risk, loan deposit ratio and cash assets ratio (not reported), and we obtain very similar results. Coleman, Esho and Sharpe (2002) use loan deposit ratio as the proxy for bank liquidity risk. The cash assets ratio is used in Kashyap, Rajan and Stein (2002) to measure bank liquid-assets.

28

strategic default on the part of the borrower by making it more costly to default. This

deterrence effect indicates that, with more banks in the syndicate, borrowers face a higher

risk of being shut out of future borrowing if they default. Taken together, one would expect

that the borrower might be at a disadvantage facing multiple banks and be charged with

higher spreads due to potential bargaining complexity, monitoring duplication or the

free-ride problem. The results of the present study confirm that the number of lenders is in

fact positively related to loan yield spreads.

4.2 Borrower and Loan Effects

As shown in Table 6, the coefficient of the current ratio is significantly negative,

consistent with our expectation and findings in prior studies. The negative sign suggests

that borrowers with lower current ratios are associated with higher loan yield spreads.

Since a borrower’s current ratio is regarded as a proxy for borrower risk, the higher the

current ratio, the lower the probability the borrower has short-term solvency or liquidity

problems.

The significant negative coefficient of borrower size is consistent with our hypothesis.

It is more difficult for banks to monitor and screen these small firms because smaller firms

presumably have more information asymmetries and more risk. Therefore, smaller

corporate borrowers are usually charged higher loan yield spreads.14

14 The variance inflation factor (VIF) for this case indicates that, multicollinearity is not a severe problem here. Furthermore, in view of the correlation between these two explanatory variables, borrower size (logarithm of borrower total assets) and relative size ratio (logarithm of bank total assets/logarithm of borrower total assets), we replace borrower size (logarithm of borrower total assets) with firm size (logarithm of borrower total sales) and obtain similar empirical

29

The results from Table 6 that deserve mention are the coefficients of the loan

characteristics variables. The statistically significant relationship between loan yield

spreads and term facility maturity is consistent with findings in Strahan (1999), Dennis

Nandy and Sharpe (2000). The negative sign of the coefficient of maturity suggests that

maturity is inversely related to loan yield spreads, as loans with longer maturity are more

likely associated with borrowers with higher credit quality.

The negative relationship between loan yield spreads and facility size is in line with

findings in Angbazo, Mei and Saunders (1998). This can be interpreted as indicating that

the originating bank is exposed to a lower level of firm-specific risk since borrowers

corresponding to large loans are more likely to be large firms which are assumed to have

lower levels of risk.

The coefficient of loan distribution, a dummy variable equal to 1 if the loan is

underwritten by a syndicate of banks, is negative. The negative sign of this coefficient can

be explained by the fact that syndicated loans lower the risk of an unsuccessful distribution

of loans due to risk diversification for bank lenders. As a result, loan yield spreads are

lower in syndicated loans.

As expected based on Table 1, a positive relationship between secured status and loan

yield spreads is obtained. This positive sign of the coefficient of secured status suggests

that since riskier borrowers are required to pledge collateral and firm specific risk cannot

be sufficiently reduced by the security guarantees, secured borrowings are positively

results (not reported).

30

associated with loan yield spreads. This positive relationship between secured status and

loan yield spreads is consistent with findings in Angbazo, Mei, and Saunders (1998),

Dennis, Nandy, and Sharpe (2000).

4.3 Robustness checks

This section discusses results from several robustness checks of the results. The first

focuses on results in three different sub-samples: term loans, revolvers, and others. The

second explores the sensitivity of the results to the single-equation OLS framework.

In this sample, as shown in Table 6, we pool term loans, revolvers and other types of

loan agreements together. This approach could be subjected to criticism. Coleman, Esho

and Sharpe (2002) suggest that the estimates obtained by Hubbard, Kuttner and Palia (2002)

could be biased as a result of their practice of imposing identical relationships across loan

types. A revolver facility provides an ongoing line of credit that may be drawn down,

repaid and re-borrowed many times over the life of the facility. The expected size of the

loan to be drawn down is often not certain as the loan amount will be associated with the

borrower’s future circumstances and the loan constraints assigned on the loan contract.

Compared to fixed term loan facilities, revolvers are more likely to be associated with

quantity risk (take-down risk) (Ho and Saunders (1983). This could lead to higher required

yield spreads. As for the focus on revolvers, Dennis, Nandy and Sharpe (2000) point out

that the contract terms on revolvers of risky firms differ from those of less risky firms.

Revolvers are very important in fostering the bank-customer relationship and in bank

31

commercial and industrial lending since the revolver, unlike the term loan, offers the

borrower the right (but not the obligation) to draw down, repay and redraw all or part of the

loan at their discretion (Rhodes (2000)). In this study, to avoid the criticism of pooling

different types of loan agreements together, we separately re-estimate the model for three

sub-samples containing term loans, revolvers and other types of loan agreements, as shown

in Table 7.15

[Table 7 here]

From Table 7, the results in the revolvers sub-sample are similar to the original

findings as shown in Table 6, suggesting that bank characteristics have significant effects

on loan yield spreads. Attention needs to be paid to the differences among the regression

results for those three sub-samples. Bank characteristics have less effect on the

determinants of loan yield spreads for the term loan and other types of loan agreement

sub-samples than for revolvers. One would expect that the effects of bank and borrower

characteristics on the setting of loan contract terms could be more accurately reflected in

revolvers. Besides the basic differences between revolvers and other types of credit loans,

the difference in sample size could also be a reason for the weak regression results for the

term loan and other types of loan sub-samples: the sample sizes for the latter two are much

smaller than that of the revolver sub-sample.

Applying the single-equation OLS technique can be subjected to criticism. Dennis,

15 A term loan is for a specific amount of money which is to be repaid in full by an agreed date. A revolver is also available for a specific amount of money for an agreed period of time, but unlike the term loan, it offers the borrower the right (but not the obligation) to draw down, repay and redraw all or part of the loan at their discretion. (Syndicated Lending, Tony Rhodes, 3rd edition)

32

Nandy and Sharpe (2000) point out the important interrelationships among contract terms.

They model maturity, secured status and pricing within a simultaneous decision framework,

documenting significant bi-directional relationships between maturity and secured status

and a uni-directional relationship from both maturity and secured status to loan pricing

(all-in-spread). Coleman, Esho and Sharpe (2002) employ OLS to investigate the effects

of bank characteristics on loan pricing and maturity. In their study they estimate the

models using OLS, assuming maturity and yield spread involve a recursive model with a

uni-directional relationship from maturity to the yield spread.

To explore the sensitivity of the results to the single-equation OLS framework and its

specifications, we estimate the model in a reduced form of a simultaneous equations

framework. Similar in spirit to the empirical approach in Coleman, Esho and Sharpe

(2002), we re-estimate our model, as the loan yield spread, maturity and secure status

involve a recursive model with uni-directional relationships from both maturity and

secured status to loan yield spreads.16 In both the maturity equation and secured status

equation, we include bank, borrower and non-price loan characteristics. The regression

results are provided in Table 8.

[Table 8]

As shown in Table 8, the results suggest that the main findings of the estimated bank

effects on loan yield spreads are robust to the reduced form framework. Thus, the results

16 In the recursive model of this study, both maturity and secured status are included in the loan yield spreads equation as explanatory variables, while loan yield spread is not included in the maturity and secured status equations.

33

do not appear to be driven by specifications or errors in the single-equation OLS

framework.

5. Conclusions

The lender-borrower relationship has been widely explored, with a focus on how it is

affected by borrower, loan and, to a lesser extent, lender characteristics. Relatively little

study has been devoted to the effects of the number of lenders in individual term loan

facilities, likely because prior work on the number of lenders has concentrated on

firm-level rather than loan contract-level data. In order to study the effects of the number

of lenders as well as other bank characteristics on loan yield spreads, we assemble loan

contract variables, borrower financial variables, and bank financial variables from the

DealScan database, the Compustat database and U.S. Federal Reserve Call Reports,

respectively. Incorporating a broader range of bank characteristics, we find that bank

characteristics have significant effects on loan yield spreads after controlling for the effects

of borrower and non-yield-spread loan characteristics. Banks with greater monitoring

power and riskier banks with lower capital-asset ratios are found to extract higher rents,

which is consistent with findings in prior studies. Importantly, a new dimension of lender

characteristics – the number of lenders – is shown to have a significant influence on loan

yield spreads.

Our study also provides evidence that borrower characteristics are important

determinants of loan yield spreads, and extends the existing literature by emphasizing the

34

significant influence of bank characteristics on loan yield spreads. Moreover, we find that

the loan type is related to the effects of borrower and bank characteristics on loan contract

terms.

35

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Table 1

Summary of Hypotheses

Hypotheses Characteristics Proxies Expected Sign Realized Sign

Borrower Characteristics

Borrower Solvency current ratio - ve - veAsymmetric Infomration borrower size - ve - veCredit Quality leverage +ve +ve

Bank Characteristics

Bank Risk relative size + ve - veBank Monitoring loan loss provision - ve - veBank Liquidity Risk capital asset ratio - ve - veMonitoring Duplicate number of lenders + ve + ve

Other Loan Characteristics

Controls TFCMAT - ve - vefacility size - ve - veloan distribution - ve - vesecured + ve + ve

40

Table 2

Description of Dependent and Explanatory Variables

Variable Description

RATEAISD Rates all-in-spread drawn, defined as the basis point coupon spread overLIBOR plus the annual fee and plus the upfront fee spread over the duration of the revolver

Current Ratio Borrowers' current assets over current liabilitiesBorrower Size Natual logarithm of borrower total assetsLeverage Borrower's total debts over total assetsTax Assets Ratio Borrower's total income taxes over total assetsRelative Size Ratio Natual logarithm of bank total assets over natual logarithm of borrower total assetsLoan Loss Provision Provision for loan and lease lossCapital Asset Ratio Bank's equity capital over total assetsNumber of Lenders The number of lead banks in each term facilityTFCMAT The term facility maturityFacility Size Natual logarithm of the amount term facility sizeLoan Distribution Dummy variable equal to 1 (0 otherwiae) if the loan is underwritten by a syndicate of banksSecured Dummy variable equal to 1 (0 otherwise) if the loan is secured

Note. This table provides a description of all the explanatory variables and dependent variable used in the cross-sectional analysis of the effects of bank, borrower, and other loan characteristics on loan yield spreads.

41

Table 3

Descriptive Statistics for Dependent and Explanatory Variables

Variable Number Mean Standard Deviation Minimum Maximum

RATEAISDa 1690 178.11 114.93 15.0 755.0Current Ratio 1523 2.17 1.80 0.10 40.67Borrower Size ($billion) 1852 2.90 12.91 0.00 257.39Leverage 1819 0.32 0.26 0.00 2.14Relative Size Ratio 1848 1.88 1.43 0.41 47.72Loan Loss Provisionb 1852 156.78 379.07 -104.0 2507Capital Asset Ratio 1848 0.07 0.02 0.03 0.27Number of Lenders 1852 3.10 3.45 1.00 28.00TFCMATc (year) 1753 3.62 2.34 0.08 20.0Facility Size ($billion) 1852 0.27 0.63 0.00001 7.00Loan Distribution 1843 0.72 0.45 0.00 1.00Secured 1235 0.74 0.44 0.00 1.00

Note. This table presents summary statistics of the explanatory and dependent variables. The loan agreements were originated during the period January1988 - December 1999.aRATEAISD is rates all-in-spread drawn. In DealScan, all-in-spread is expressed as a spread over LIBOR which takes into accountsboth one-time and recurring fees associated with the loan.bLoan Loss Provision is a measure of bank loan quality. It is the responsibility of the bank's management to determine an adequateloan and lease loss provision based on current knowledge of the bank's loan portfolios.cTFCMAT is the term facility maturity.

42

Table 4

Comparison of Selected Variables in Our Sample and Other Sample

Variable Mean S.D. Minimum MaximumA B A B A B A B

RATEAISD (b.p) 178.11 125.50 114.93 93.91 15.00 2.68 755.00 505.40TFCMAT(year) 3.62 4.12 2.34 2.10 0.08 0.11 20.00 30.00Facility Size ($million) 271.06 361.30 634.18 700.20 0.01 0.60 7000 10500Borrower Size 5.74 20.54 2.07 1.89 0.18 14.91 12.46 26.15Tax Assets Ratio 0.02 0.03 0.04 0.03 -0.17 -0.20 0.35 0.21Cash Assets Ratio 0.08 0.09 0.04 0.04 0.01 0.02 0.22 0.20Loan Deposit Ratio 0.89 0.89 0.21 0.14 0.32 0.42 2.80 1.38Bank Size 9.14 25.23 1.67 1.02 3.06 18.04 13.00 26.21Number of Lenders 3.10 1.77 3.45 1.11 1.00 1.00 28.00 19.00Relative Size Ratio 1.88 437.10 1.43 931.00 0.41 0.01 47.72 9681

Bank Total Assets ($billion) 31.98 90.60Borrower Total Assets ($billion) 2.90 0.83Percentage of Syndicated Loan 0.72 0.92Borrower Sales ($billion) 1.90Bank Total Loans ($billion) 21.60Bank Total Deposit ($billion) 22.60Note. This table presents the comparison of selected variables from out sample and the sample in Coleman, Esho and Sharpe (2002). Column A contains the summary statistics of the variables from our sample. Column B contains the summary statistics of the variables in the sample of Coleman, Esho and Sharpe (2002).Bank size, borrower size, and facility size are the Natual logarithm of bank total assets, borrower total assets,and term loan facility amount size.

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Table 5

Correlation Matrix : Full Sample

Relative Loan Capial NumberCurrent Borrower Size Loss Asset of Facility Loan

RATEAISD Ratio Size Leverage Ratio Provision Ratio Lenders TFCMAT Size Distribution SecuredRATEAISD 1Current Ratio 0.06604 1Borrower Size -0.63589 -0.21197 1Leverage -0.02288 -0.31794 0.13839 1Relative Size Ratio 0.31599 0.06152 -0.56556 -0.04899 1Loan Loss Provision -0.05896 0.10264 -0.05549 -0.05719 0.11194 1Capital Assets Ratio -0.11564 -0.10594 0.10052 0.05794 -0.03296 -0.05541 1Number of Lenders -0.38491 -0.20579 0.63195 0.20558 -0.29643 -0.13544 0.06118 1TFCMAT -0.19516 -0.04493 0.2011 0.12213 -0.15281 0.02164 0.05093 0.1714 1Facility Size -0.64443 -0.19868 0.82855 0.21899 -0.44416 -0.03704 0.116 0.65332 0.29947 1Loan Distribution -0.51486 -0.18317 0.60376 0.20865 -0.36894 0.04824 0.08299 0.37784 0.3197 0.72075 1Secured 0.48162 0.06893 -0.38457 0.03247 0.17024 -0.01946 -0.0822 -0.2243 0.06355 -0.31158 -0.19542 1

Table 6OLS Estimations of the Determinants of Loan Yield Spread : Full Sample

Variable Regression 1 Regression 2 Regression 3 regression 4

Intercept 287.48 348.87 247.88 274.18

(20.85)*** (18.30)*** (32.81)*** (21.94)***Borrower CharacteristicsCurrent Ratio -4.78 -4.02

(-2.57)*** (-2.19)**Borrower Size -12.33 -20.61

(-4.11)*** (-6.19)***Leverage 13.69 4.30

(1.26) (0.40)Bank CharacteristicsRelative Size Ratio -4.36 0.8

(-2.49)** (0.51)

Loan Loss Provision -0.03 -0.02

(-3.89)*** (-4.10)***

Capital Asset Ratio -230.21 -317.49

(-1.74)* (-2.46)**

Number of Lenders 8.00 4.24

(5.25)*** (3.34)***

Other Loan CharacteristicsTFCMAT -0.21 -0.25 -0.19 -0.19

(-1.83)* (-2.19)** (-1.89)* (-1.90)*

Facility Size -14.66 -18.43 -22.26 -27.09

(-5.21)*** (-6.38)*** (-12.85)*** (-12.30)***

Loan Distribution -33.01 -20.56 -38.14 -29.25

(-3.85)*** (-2.39)** (-4.89)*** (-3.71)***

Secured 89.3 86.42 87.55 86.44

(12.87)*** (12.74)*** (15.09)*** (15.04)***

Adjusted R-square 0.5099 0.5373 0.5051 0.5205

Number of Observations 952 948 1144 1140

F value 142.47 101.07 292.93 155.69

Pr > F <.0001 <.0001 <.0001 <.0001 This table shows the estimates of the effects of bank, borrower and non-price loan characteristics on the determinants of loan yield spreads. ***, **, * indicate significance at 1%, 5%, and 10% levels, respectively. t-statistics are calculated using White's heteroskedasticity-consistent standard errors. Sample sizes for these regressions vary on the basis of the availability of all explanatory variables for each regression.

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Table 7OLS Estimations of the Determinants of Loan Yield Spread : Subsamples

Term Loans Revolvers Others

Variable Regression 1 Regression 2 Regression 3

Intercept 555.42 353.27 251.03

(7.78)*** (16.38)*** (3.63)***Borrower CharacteristicsCurrent Ratio -5.65 -4.94 -5.83

(-1.27) (-2.26)** (-1.10)

Borrower Size -45.36 -21.79 3.35

(-4.74)*** (-5.59)*** (0.24)

Leverage -12.30 13.95 -39.75

(-0.50) (1.12) (-1.04)

Bank CharacteristicsRelative Size Ratio -36.97 -3.74 -1.31

(-3.12)*** (-2.14)** (-0.17)

Loan Loss Provision -0.02 -0.02 -0.0006

(-0.99) (-3.06)*** (0.02)

Capital Asset Ratio -76.56 -210.05 -214.78

(-0.21) (-1.32) (-0.44)

Number of Lenders 4.25 8.82 6.57

(1.19) (4.89)*** (1.28)Other Loan CharacteristicsTFCMAT -0.81 -0.29 0.11

(-2.84)*** (-1.86)* (0.20)Facility Size -1.39 -19.15 -27.54

(-0.21) (-4.95)*** (-2.77)***Loan Distribution -27.58 -17.01 -65.24

(-1.33) (-1.68)* (-2.26)**Secured 78.44 75.15 147.89

(3.76)*** (9.79)*** (6.65)***Adjusted R-square 0.4276 0.5372 0.6883

Number of Obs 221 623 104

F value 16.01 66.52 21.87

Pr > F <.0001 <.0001 <.0001

Note. This table presents the estimates of the effects of bank, borrower and other loan characteriston the determinants of loan yield spreads in three sub-samples, term loans, revolvers and others.***, **, * indicate significance at 1%, 5%, and 10% levels, respectively.t-statistics are calculated using White's heteroskedasticity-consistent standard errors.

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Table 8OLS Estimations of the Determinants of Loan Yield Spreads : Full Sample

Single-Equation OLS Reduced FormVariable Yield Spread Maturity Secured StatusIntercept 348.87 339.22 21.21 1.2

(18.30)*** (17.36)*** (4.03)*** (13.29)***Borrower CharacteristicsCurrent Ratio -4.02 -4.80 0.85 0.009

(-2.19)** (-2.40)** (1.44) (0.86)Borrower Size -20.61 -20.91 -3.28 -0.13

(-6.19)*** (-5.99)*** (-3.28)*** (-7.70)***Leverage 4.30 3.41 7.33 0.2

(0.40) (0.31) (2.26)** (3.60)***Tax Asset Ratio 5.82

(0.30)Intangible Ratio 0.06

(4.52)***Bank CharacteristicsRelative Size Ratio -4.36 -3.34 -0.72 -0.01

(-2.49)** (-2.00)** (-1.45) (-1.35)Loan Loss Provision -0.03 -0.02 0.004 -0.000006

(-3.89)*** (-2.98)*** (1.7)* (-0.17)Capital Asset Ratio -230.21 -180.85 103.74 0.80

(-1.74)* (-1.22) (2.37)** (1.06)Number of Lenders 8.00 9.12 0.95 -0.01

(5.25)*** (5.64)*** (1.98)** (-1.21)Other Loan CharacteristicsTFCMAT -0.25 -0.1

(-2.19)** (-0.81)Facility Size -18.43 -20.45 3.95 0.03

(-6.38)*** (-6.41)*** (4.20)*** (1.61)Loan Distribution -20.56 -13.43 15.46 0.05

(-2.39)** (-1.50) (5.96)*** (1.12)Secured 86.42 85.25

(12.74)*** (11.93)***Adjusted R-square 0.5373 0.5636 0.2363 0.2048Number of Obs 948 754 754 754F value 101.07 89.54 24.33 20.42Pr > F <.0001 <.0001 <.0001 <.0001Note. This table shows the estimates of the effects of bank, borrower and other loan characteristics on the determinants of loan yield spreads in both single-equation OLS framework and reducedform framework. ***, **, * indicate significance at 1%, 5%, and 10% levels, respectively.t-statistics are calculated using White's heteroskedasticity-consistent standard errors.

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